Radiation imaging technology is widely used in security inspection and medical fields, and the article recognition capability of imaging systems is an important criterion for measuring system indicators.
With the continuous development of photon counting detector technology such as CZT, multi-energy spectrum imaging has many advantages in reducing radiation dose and improving the article recognition capability etc., and has a wide application prospect. The multi-energy spectrum imaging can divide an energy spectrum of received X-rays into a plurality of energy regions and count them separately to obtain ray attenuation information corresponding to different energy regions. Compared with the conventional dual-energy imaging, the multi-energy spectrum imaging substantially eliminates the overlapping between the energy spectrums, and has better energy discriminability between different energy regions, thereby significantly improving the article recognition capability of the system, while dividing the energy spectrum into more energy regions as required, and providing conditions for the introduction of more energy information.
A number of energy regions into which an energy spectrum is divided through existing multi-energy spectrum imaging may be from three to five in the early stage to several to 256 at present. In general, due to the use of more energy information, more refined energy spectrum division can not only bring higher article recognition capability for the system, but also cause higher cost and more difficulty for the system design and data processing.
French research institution CEA-Leti proposed that in a case that there are only a small number of energy regions, the article recognition capability of the system after optimizing an energy spectrum threshold can be significantly improved compared with the equal division of an energy spectrum. For a selected target material, an optimized five-energy region system can also have a higher article recognition capability, but the optimized threshold parameters may only be applied to recognition of materials that are closer to the selected target material. If a more refined energy region division is used, the system has a stronger applicability for the recognition of different materials, but this will result in a greater system overhead. At present, the optimization methods involved in the research of parameter optimization are mainly a CIP method and a CRC method based on a recognition process. Parameters obtained by different optimization methods are very close.
For a particular application scenario, in most cases, there is no need for a very refined energy region division, but a few divided energy regions are used while preforming optimization and adjustment on the threshold parameters, which can also enable the system to achieve a better article recognition effect.